- Email: [email protected]
Evaluation of Nutritional Status in Renal Transplant Recipients in Accordance with Changes in Graft Function
Evaluation of Nutritional Status in Renal Transplant Recipients in Accordance with Changes in Graft Function E. Tutal, S. Sezer, M.E. Uyar, Z. Bal, B.G. Demirci, and F.N.O. Acar ABSTRACT Introduction and Aims. Renal transplantation (RT) is the ultimate treatment modality for end-stage renal disease (ESRD) patients. Malnutrition is a strong predictor of cardiovascular disease among ESRD patients. Body composition analysis using bioimpedance devices (BIA) is a useful noninvasive tool to detect malnutrition in this population. We investigated the impact of graft function on nutritional status and reliability of BIA to detect malnutrition in RT recipients. Methods. We evaluated retrospectively 189 RT recipients including 59 females, and of overall mean age of 38.3 ⫾ 10.6 years who had a minimum posttransplant follow-up of 12 months. Body Composition Analyzer (Tanita BC-420MA) determinations were complemented with monthly assessments of biochemical parameters. Patients were divided into 3 groups according to glomerular filtration rate (GFR) levels: normal graft function/high GFR group (group 1, normal creatinine levels, no proteinuria and GFR ⱖ 90 mL/min; n ⫽ 59); low renal function/low GFR group (normal or high creatinine levels with low GFR levels; group 2; GFR 89 – 60 mL/min; n ⫽ 87) and group 3, (GFR ⬍ 60 mL/min; n ⫽ 43). Results. Group 3 patients displayed significantly lower as well as hemoglobin albumin and calcium concentrations, with higher phosphorus and parathyroid hormone levels (P ⫽ .0001). They also showed significantly lower body weight (P ⫽ .0001), body mass index (P ⫽ .002), fat (P ⫽ .002) and muscle (P ⫽ .0001) contents as well as fat-free mass (P ⫽ .0001). Group 2 patients had significantly lower values compared with group 1 and higher values than group 3. GFR values positively correlated with albumin, fat, muscle, and fat-free mass (r ⫽ 0.337, 0.299, 0.281, 0.278, respectively; P ⫽ .0001). GFR values positively correlated with visceral fat ratio (r ⫽ 0.170; P ⫽ 0.02), body mass index (r ⫽ 0.253; P ⫽ .0001), and waist-hip ratio (r ⫽ 0.218; P ⫽ .006). Conclusion. Loss of muscle and fat mass is an early sign of malnutrition among RT recipients. It is closely associated with loss of GFR. BIA is a noninvasive and reliable diagnostic tool that should be included in the follow-up of these patients for an early diagnosis of malnutrition-related complications. ENAL TRANSPLANTATION (RT) is the ultimate treatment modality for end-stage renal disease (ESRD) patients who require maintenance hemodialysis treatments (MHD). Cardiovascular events or graft failure requiring dialysis treatment after RT are frequent problems. Nutritional status is known to be a marker of overall health status in ESRD.1 Malnutrition is a strong predictor of both cardiovascular and all-cause mortality.1–3 Patients on MHD show an association between body mass index (BMI) and mortality. A lower but not a higher BMI is predictive of mortality among HD patients.4 Nevertheless,
R
the relation between nutritional status and allograft survival after RT is controversial. Although malnutrition is frequent in ESRD, body weight, and other nutritional parameters
From the Department of Nephrology (E.T., S.S., M.E.U., Z.B., B.G.D.), Baskent University Faculty of Medicine, Ankara, Turkey; and Department of Nephrology (F.N.O.A.), Baskent University Faculty of Medicine, Istanbul, Turkey. Address reprint requests Emre Tutal, MD, Baskent University Faculty of Medicine, Department of Nephrology, Besevler, Ankara, 06490, Turkey. E-mail: [email protected]
0041-1345/13/$–see front matter http://dx.doi.org/10.1016/j.transproceed.2013.01.104
© 2013 by Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010-1710
1418
Transplantation Proceedings, 45, 1418 –1422 (2013)
EVALUATION OF NUTRITIONAL STATUS
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usually improve after successful RT due to improved appetite and reversal of uremia.5 Body composition, a parameter that reflects nutritional status, also changes in a favorable manner after RT. The accompanying increase in dietary intake combined with corticosteroid therapy lead to increased body fat.6 Body composition analysis with bioimpedance devices (BIA) is a useful noninvasive tool for early detection of malnutrition in both normal and ESRD populations.7 However, there are insufficient data about the use of BIA in RT recipients as we investigated in this study to detect malnutrition among RT recipients. METHODS We enrolled 189 RT recipients who had over 12 months of follow-up after RT for anthropometric and body composition analysis using the Body Composition Analyzer (Tanita BC420MA). To calculate waist and hip circumferences, sagittal abdominal diameter, body weight, fat mass, fat ratio, fat-free mass, muscle mass, visceral fat ratio, and BMI. We assessed monthly but retrospectively collected biochemical parameters including hemoglobin, serum creatinine, albumin, C-reactive protein (CRP), calcium, phosphorus, parathyroid hormone (PTH) level, lipid profile of the last 6 months, calculating their mean values. Patients were divided into 3 groups according to glomerular filtration rate (GFR) levels as normal graft function/high GFR group (group 1, normal creatinine levels, no proteinuria and GFR ⱖ 90 mL/min, n: 59); low renal function/low GFR groups (normal or high creatinine levels
with low GFR levels, group 2 GFR 89 – 60 mL/min; n ⫽ 87), and group 3, (GFR ⬍ 60 mL/min; n ⫽ 43). Statistical analyses were performed using SPSS software (Statistical Package for the Social Sciences, version 11.0, SSPS Inc, Chicago, Ill). Normality of data was analyzed by using a Kolmogorov-Smirnov test. All numerical variables with normal distributions were expressed as mean values ⫾ standard deviations, while those with skew distributions as medians and interquartile range. Categorical variables expressed as percentages were compared by chi-square tests. Normally distributed numeric variables were analyzed by independent sample t or one-way analysis of variance (post-hoc Tukey) tests according to distribution normality. Skew distributed numeric variables were compared using the Mann– Whitney U and Kruskal–Wallis tests according to distribution normality. Spearman and Pearson correlation tests were used for correlation analyses. A linear regression analysis was employed to identify the main factors that affected albumin levels. P ⬍ .05 was considered to be statistically significant.
RESULTS
Study groups were similar for gender and age distribution; however group 3 patients showed significantly longer posttransplant follow-ups compared with groups 1 and 2 (Table 1). Group 3 patients also displayed significantly lower albumin, calcium and hemoglobin levels as well as higher phosphorus and PTH concentrations compared (Table 1). There was no significant difference between groups 1 and 2 in their mean values of biochemical parameters except hemoglobin and calcium levels which were signifi-
Table 1. Demographic Data, Laboratory Values, Anthropometric Measurements, and Body Composition Analyses of Study Groups
Gender (F/M) Age (yrs) Transplantation age (mos) Albumin (g/dL) Creatinine (mg/dL) GFR (mL/min) Calcium (mg/dL) Phosphorus (mg/dL) PTH (pg/mL) ALP (U/l) Hemoglobin (g/dL) HDL cholesterol (mg/dL) LDL cholesterol (mg/dL) Trigliceride (mg/dL) Total cholesterol (mg/dL) Waist circumference (cm) Hip circumference (cm) Waist-hip ratio Sagitto-abdominal dm. (cm) Body weight (kg) Fat mass (kg) Fat ratio (%) Fat free mass (kg) Muscle mass (kg) Visceral fat ratio (%) BMI (kg/m2)
Group 1 (n ⫽ 59)
Group 2 (n ⫽ 87)
Group 3 (n ⫽ 43)
P
13/46 36.1 ⫾ 10.4 25 (32) 4.2 (0.3) 0,9 ⫾ 0,2 110.1 ⫾ 16.5 9.5 (0.9) 3.1 ⫾ 0.6 125 (93.7) 82.5 (43.2) 14,3 ⫾ 1,9 44,9 ⫾ 11,0 115,2 ⫾ 28,5 164 (75) 201 (40) 97.0 ⫾ 12.5 103.3 ⫾ 8.7 0.93 ⫾ 0.07 23.6 ⫾ 4.1 77.7 ⫾ 14.4 18.6 ⫾ 8.8 23.3 ⫾ 8.7 59.1 ⫾ 10.1 56.1 ⫾ 9.6 7.2 ⫾ 4.6 27.0 ⫾ 4.6
28/59 39.0 ⫾ 10.6 34 (51) 4.3 (0.33) 1.2 ⫾ 0.2 76.2 ⫾ 7.5 9.2 (0.7) 3.1 ⫾ 0.5 120 (75.1) 74 (37.2) 13.3 ⫾ 1.6 47.7 ⫾ 16.6 117.3 ⫾ 35.6 159 (76) 207.6 (58) 90.4 ⫾ 11.9 99.1 ⫾ 7.4 0.91 ⫾ 0.08 21.8 ⫾ 3.4 70 ⫾ 12.3 16.4 ⫾ 8.4 22.7 ⫾ 8.8 53.1 ⫾ 9.8 50.9 ⫾ 8.2 6.7 ⫾ 4.3 25.6 ⫾ 5.7
18/25 40.0 ⫾ 10.6 41 (93.5) 3.9 (0.6) 2.1 ⫾ 0.9 43.8 ⫾ 11.5 9.1 (1) 3.5 ⫾ 0.8 175.5 (220.1) 71 (33) 11.8 ⫾ 1.7 47.6 ⫾ 12.5 119.0 ⫾ 33.7 145 (81) 199 (65) 85.1 ⫾ 13.3 96.7 ⫾ 8.5 0.87 ⫾ 0.08 21.1 ⫾ 4.0 64.3 ⫾ 13.3 12.6 ⫾ 7.1 18.8 ⫾ 9.5 51.6 ⫾ 9.4 49.1 ⫾ 9 4.9 ⫾ 3.3 23.4 ⫾ 4.3
.099 .134 .0001 .0001 .0001 .0001 .0001 .042 .009 .149 .0001 .473 .849 .297 .705 .0001 .001 .005 .019 .0001 .002 .023 .0001 .0001 .025 .002
Abbreviations: ALP, alkaline phosphatase; BMI, body mass index; GFR, glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; PTH, parathyroid hormone.
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cantly lower in group 2 (Table 1). Group 3 patients demonstrated significantly lower mean values of body weights and BMI compared with the other groups (P ⫽ .0001 and 0.002, respectively; Table 1). Body composition analysis revealed group 3 patients to display significantly lower fat, muscle, and fat-free mass values (P ⫽ .002, .0001 and .0001 respectively; Table 1). Similar to biochemical parameters, group 2 patients were between groups 1 and 3 in terms of mean results of anthropometric measures; they showed significantly lower values compared with group 1 and higher versus results group 3 (Table 1). Correlation analysis
Fig 1.
revealed GFR values to positively correlate with albumin, fat, muscle, and fat-free mass values (r ⫽ 0.337, 0.299, 0.281, and 0.278, respectively; P ⫽ .0001; Fig 1). GFR values also positively correlated with visceral fat ratio (r ⫽ 0.170; P ⫽ .02; Fig 1). BMI (r ⫽ 0.253; P ⫽ .0001) and waist-hip ratio (r ⫽ 0.218; P ⫽ .006). We performed a linear regression analysis to identify the main factors among age, creatinine, and PTH levels, GFR values, and BIA parameters that affect albumin levels. This analysis showed GFR values ( ⫽ 0.289; r2 ⫽ 0.109; P ⫽ .0001) and age ( ⫽ ⫺0.199; r2 ⫽ 0.147; P ⫽ .007) to be the main predictors for low albumin levels.
Correlation of glomerular filtration rate (GFR) and visceral fat ratios.
EVALUATION OF NUTRITIONAL STATUS
DISCUSSION
RT improves the prognosis for ESRD patients. Weight gain after RT is common; however, 15%–23% of recipients show symptoms of malnutrition.8,9 Significant weight gain in the post-transplant period is assumed to be a consequence of both increased appetite caused by steroid administration and correction of uremia that reconstitutes body components.6 Obesity is a well-known risk factor for morbidity and mortality in the general population; however, being overweight or obese is associated with increased survival among dialysis patients.4,10 The impact of weight gain on maintenance or loss of graft function graft function are controversial. In RT recipients. Body composition analysis with bioimpedance devices (BIA) is a useful noninvasive tool that can be used for early detection of malnutrition in both normal and chronic kidney disease populations;7 however, there are presently limited data on nutritional status and body composition measurements using BIA in RT recipients. Patients with varying degrees of renal function, show correlations of the values of free fat mass and body cell mass with 24-hour urinary creatinine excretion and muscle mass.11 Furthermore BIA has been documented to be suitable to measure total body fluids, while their is controversy about its use for body fat and fat-free mass estimates.11 The present study showed GFR values to positively correlate with fat and fat-free as well as muscle masses that suggesting graft function predicts nutritional status. Lucchesi et al11 observed reduced BMI values due to a reduction in fat mass, an alteration that was independent of the level of graft function. The present study indicated that development of malnutrition with a significant decrease in body weight and body fat followed by a decreased muscle mass were closely associated with declining graft function. We detected significant reductions in body weight, BMI, fat, and bone and muscle mass among patients with lower GFR values. Nevertheless, visceral fat ratio and BMI directly correlated with GFR. Similar to our results, Rettkowski et al6 observed impaired long-term kidney graft survival among patients with reduced nutritional status. Although they initially showed no significant influence of BMI on the risk of transplant failure at 1 year after kidney grafting, when patients were divided into 2 groups according to BMI, there was a clear disadvantage in long-term graft survival for subjects with BMI ⬍ 23.6 In contrast with our results, Ducloux et al12 demonstrated decreased GFR values among patients with substantial weight gain in the first year after RT. More than a 5% increase in BMI was associated with a 3-fold increase in the risk of graft loss. As a limitation of the study, they only used BMI rather than body composition analysis or anthropometric parameters.12 The conflicting data may be explained: patients with similar BMI may have different visceral fat, muscle, or bone mass. In another study, Marcen et al10 did not observe a significant difference between weight gain and survival among RT
1421
patients. In our study, RT patients with lower GFR values also demonstrated lower serum albumin levels, indicating that graft function was directly correlated with nutritional status. Similar to our results Mantoo et al. showed increased intact PTH levels, decreased albumin, mean serum bicarbonate, and hemoglobin levels that may eventually affect nutritional status to be related to renal allograft dysfunction. In addition they emphasized the impact of psychological factors, such as depression, socioeconomic factors, and inadequate dietary prescription to cause posttransplant malnutrition.13 Association of GFR loss and malnutrition is well known among predialysis chronic kidney disease patients. In a subanalysis of the Modification of Diet in Renal Disease study Kopple et al14 reported that patients with chronic renal disease shown progressively declining dietary protein and energy intakes as well as serum and anthropometric measures of protein– energy nutritional status GFR decreases. They also reported that low GFR levels were significantly associated with antrophometric markers of malnutrition like BMI and arm circumference. Similarly, Garg et al15 analyzed 5248 predialysis chronic kidney disease patients reporting that a GFR ⬍ 30 mL/min was independently associated with malnutrition after adjustment for relevant demographic, social, and medical conditions. We believe that dietary restrictions or increased medication needs in early phases of graft dysfunction can lead to a decreased appetite and food intake, these factors might decrease body weight including global decreases in all body compartments. Malnutrition accelerates due to progressive loss and addition of uremic symptoms of nausea, vomiting and decreased appetite. According to our findings especially in patients with moderate graft dysfunction (group 2), BIA was a useful noninvasive early diagnostic tool to diagnose malnutrition tendencies even before other markers like hypoalbuminemia become clinically apparent. In conclusion, we observed nutritional status to be closely related to loss of graft function among RT recipients. We suggest that BIA is an appropriate noninvasive diagnostic tool for early diagnosis of malnutrition in RT patients.
REFERENCES 1. Stenvinkel P, Heimburger O, Paultre F, et. al. Strong association between malnutrition, inflammation, and atherosclerosis in chronic renal failure. Kidney Int. 1999;55:1899 –1911. 2. Kovesdy CP, George SM, Anderson JE, et al. Outcome predictability of biomarkers of protein-energy wasting and inflammation in moderate and advanced chronic kidney disease. Am J Clin Nutr. 2009;90:407– 414. 3. Kalantar-Zadeh K, Kopple JD. Relative contributions of nutrition and inflammation to clinical outcome in dialysis patients. Am J Kidney Dis. 2001;38:1343–1350. 4. Dalrymple LS, Kaysen GA. Obesity and mortality on dialysisdoes age matter? Nat Rev Nephrol. 2012;8:258 –260. 5. Harada H, Nakamura M, Hotta K, et al. Percentages of water, muscle, and bone decrease and lipid increases in early period after successful kidney transplantation: a body composition analysis. Transplant Proc. 2012;44:672– 675.
1422 6. Rettkowski O, Wienke A, Hamza A, et al. Low body mass index in kidney transplant recipients: risk or advantage for longterm graft function. Transplant Proc. 2007;39:1416 –1420. 7. Dumler F. Use of bioelectric impedance analysis and dualenergy X-ray absorptiometry for monitoring the nutritional status of dialysis patients. ASAIO J. 1997;43:256 –260. 8. Djukanovic L, Lezaik V, Blagojevic R, et al. Co-morbidity and kidney graft failure-two main causes of malnutrition in kidney transplant patients. Nephrol Dial Transplant. 2003;68(suppl):5. 9. Van Dem Ham EC, Koman JP, Christiaans MH, et al. Weight changes after renal transplantation: a comparison between patients on 5- maintenance steriod therapy and that on steroid-free immunosuppressive therapy. Transpl Int. 2003;16:300. 10. Marcen R, Fernandez A, Pascual J, et al. High body mass index and posttransplant weight gain are not risk factors for kidney graft and patient outcome. Transplant Proc. 2007;39:2205–2207.
TUTAL, SEZER, UYAR ET AL 11. Lucchesi A, Ardini M, Donadio E, et al. Nutritional status in renal transplant recipients, evaluated by means of body composition analysis. Transplant Proc. 2001;33:3398 –3399. 12. Ducloux D, Kazory A, Simula-Faivre D, et al. One-year post-transplant weight gain is a risk factor for graft loss. Am J Transplant. 2005;5:2922–2928. 13. Mantoo S, Abraham G, Pratap GB, et al. Nutritional status in renal transplant recipients. Saudi J Kidney Dis Transplant. 2007;18:382–386. 14. Kopple JD, Greene T, Chumlea WC, et al. Relationship between nutritional status and the glomerular filtration rate: results from the MDRD study. Kidney Int. 2000;57:1688 –1703. 15. Garg AX, Blake PG, Clark WF, et al. Association between renal insufficiency and malnutrition in older adults: results from the NHANES III. Kidney Int. 2001;60:1867–1874.
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the relation between nutritional status and allograft survival after RT is controversial. Although malnutrition is frequent in ESRD, body weight, and other nutritional parameters
From the Department of Nephrology (E.T., S.S., M.E.U., Z.B., B.G.D.), Baskent University Faculty of Medicine, Ankara, Turkey; and Department of Nephrology (F.N.O.A.), Baskent University Faculty of Medicine, Istanbul, Turkey. Address reprint requests Emre Tutal, MD, Baskent University Faculty of Medicine, Department of Nephrology, Besevler, Ankara, 06490, Turkey. E-mail: [email protected]
0041-1345/13/$–see front matter http://dx.doi.org/10.1016/j.transproceed.2013.01.104
© 2013 by Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010-1710
1418
Transplantation Proceedings, 45, 1418 –1422 (2013)
EVALUATION OF NUTRITIONAL STATUS
1419
usually improve after successful RT due to improved appetite and reversal of uremia.5 Body composition, a parameter that reflects nutritional status, also changes in a favorable manner after RT. The accompanying increase in dietary intake combined with corticosteroid therapy lead to increased body fat.6 Body composition analysis with bioimpedance devices (BIA) is a useful noninvasive tool for early detection of malnutrition in both normal and ESRD populations.7 However, there are insufficient data about the use of BIA in RT recipients as we investigated in this study to detect malnutrition among RT recipients. METHODS We enrolled 189 RT recipients who had over 12 months of follow-up after RT for anthropometric and body composition analysis using the Body Composition Analyzer (Tanita BC420MA). To calculate waist and hip circumferences, sagittal abdominal diameter, body weight, fat mass, fat ratio, fat-free mass, muscle mass, visceral fat ratio, and BMI. We assessed monthly but retrospectively collected biochemical parameters including hemoglobin, serum creatinine, albumin, C-reactive protein (CRP), calcium, phosphorus, parathyroid hormone (PTH) level, lipid profile of the last 6 months, calculating their mean values. Patients were divided into 3 groups according to glomerular filtration rate (GFR) levels as normal graft function/high GFR group (group 1, normal creatinine levels, no proteinuria and GFR ⱖ 90 mL/min, n: 59); low renal function/low GFR groups (normal or high creatinine levels
with low GFR levels, group 2 GFR 89 – 60 mL/min; n ⫽ 87), and group 3, (GFR ⬍ 60 mL/min; n ⫽ 43). Statistical analyses were performed using SPSS software (Statistical Package for the Social Sciences, version 11.0, SSPS Inc, Chicago, Ill). Normality of data was analyzed by using a Kolmogorov-Smirnov test. All numerical variables with normal distributions were expressed as mean values ⫾ standard deviations, while those with skew distributions as medians and interquartile range. Categorical variables expressed as percentages were compared by chi-square tests. Normally distributed numeric variables were analyzed by independent sample t or one-way analysis of variance (post-hoc Tukey) tests according to distribution normality. Skew distributed numeric variables were compared using the Mann– Whitney U and Kruskal–Wallis tests according to distribution normality. Spearman and Pearson correlation tests were used for correlation analyses. A linear regression analysis was employed to identify the main factors that affected albumin levels. P ⬍ .05 was considered to be statistically significant.
RESULTS
Study groups were similar for gender and age distribution; however group 3 patients showed significantly longer posttransplant follow-ups compared with groups 1 and 2 (Table 1). Group 3 patients also displayed significantly lower albumin, calcium and hemoglobin levels as well as higher phosphorus and PTH concentrations compared (Table 1). There was no significant difference between groups 1 and 2 in their mean values of biochemical parameters except hemoglobin and calcium levels which were signifi-
Table 1. Demographic Data, Laboratory Values, Anthropometric Measurements, and Body Composition Analyses of Study Groups
Gender (F/M) Age (yrs) Transplantation age (mos) Albumin (g/dL) Creatinine (mg/dL) GFR (mL/min) Calcium (mg/dL) Phosphorus (mg/dL) PTH (pg/mL) ALP (U/l) Hemoglobin (g/dL) HDL cholesterol (mg/dL) LDL cholesterol (mg/dL) Trigliceride (mg/dL) Total cholesterol (mg/dL) Waist circumference (cm) Hip circumference (cm) Waist-hip ratio Sagitto-abdominal dm. (cm) Body weight (kg) Fat mass (kg) Fat ratio (%) Fat free mass (kg) Muscle mass (kg) Visceral fat ratio (%) BMI (kg/m2)
Group 1 (n ⫽ 59)
Group 2 (n ⫽ 87)
Group 3 (n ⫽ 43)
P
13/46 36.1 ⫾ 10.4 25 (32) 4.2 (0.3) 0,9 ⫾ 0,2 110.1 ⫾ 16.5 9.5 (0.9) 3.1 ⫾ 0.6 125 (93.7) 82.5 (43.2) 14,3 ⫾ 1,9 44,9 ⫾ 11,0 115,2 ⫾ 28,5 164 (75) 201 (40) 97.0 ⫾ 12.5 103.3 ⫾ 8.7 0.93 ⫾ 0.07 23.6 ⫾ 4.1 77.7 ⫾ 14.4 18.6 ⫾ 8.8 23.3 ⫾ 8.7 59.1 ⫾ 10.1 56.1 ⫾ 9.6 7.2 ⫾ 4.6 27.0 ⫾ 4.6
28/59 39.0 ⫾ 10.6 34 (51) 4.3 (0.33) 1.2 ⫾ 0.2 76.2 ⫾ 7.5 9.2 (0.7) 3.1 ⫾ 0.5 120 (75.1) 74 (37.2) 13.3 ⫾ 1.6 47.7 ⫾ 16.6 117.3 ⫾ 35.6 159 (76) 207.6 (58) 90.4 ⫾ 11.9 99.1 ⫾ 7.4 0.91 ⫾ 0.08 21.8 ⫾ 3.4 70 ⫾ 12.3 16.4 ⫾ 8.4 22.7 ⫾ 8.8 53.1 ⫾ 9.8 50.9 ⫾ 8.2 6.7 ⫾ 4.3 25.6 ⫾ 5.7
18/25 40.0 ⫾ 10.6 41 (93.5) 3.9 (0.6) 2.1 ⫾ 0.9 43.8 ⫾ 11.5 9.1 (1) 3.5 ⫾ 0.8 175.5 (220.1) 71 (33) 11.8 ⫾ 1.7 47.6 ⫾ 12.5 119.0 ⫾ 33.7 145 (81) 199 (65) 85.1 ⫾ 13.3 96.7 ⫾ 8.5 0.87 ⫾ 0.08 21.1 ⫾ 4.0 64.3 ⫾ 13.3 12.6 ⫾ 7.1 18.8 ⫾ 9.5 51.6 ⫾ 9.4 49.1 ⫾ 9 4.9 ⫾ 3.3 23.4 ⫾ 4.3
.099 .134 .0001 .0001 .0001 .0001 .0001 .042 .009 .149 .0001 .473 .849 .297 .705 .0001 .001 .005 .019 .0001 .002 .023 .0001 .0001 .025 .002
Abbreviations: ALP, alkaline phosphatase; BMI, body mass index; GFR, glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; PTH, parathyroid hormone.
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cantly lower in group 2 (Table 1). Group 3 patients demonstrated significantly lower mean values of body weights and BMI compared with the other groups (P ⫽ .0001 and 0.002, respectively; Table 1). Body composition analysis revealed group 3 patients to display significantly lower fat, muscle, and fat-free mass values (P ⫽ .002, .0001 and .0001 respectively; Table 1). Similar to biochemical parameters, group 2 patients were between groups 1 and 3 in terms of mean results of anthropometric measures; they showed significantly lower values compared with group 1 and higher versus results group 3 (Table 1). Correlation analysis
Fig 1.
revealed GFR values to positively correlate with albumin, fat, muscle, and fat-free mass values (r ⫽ 0.337, 0.299, 0.281, and 0.278, respectively; P ⫽ .0001; Fig 1). GFR values also positively correlated with visceral fat ratio (r ⫽ 0.170; P ⫽ .02; Fig 1). BMI (r ⫽ 0.253; P ⫽ .0001) and waist-hip ratio (r ⫽ 0.218; P ⫽ .006). We performed a linear regression analysis to identify the main factors among age, creatinine, and PTH levels, GFR values, and BIA parameters that affect albumin levels. This analysis showed GFR values ( ⫽ 0.289; r2 ⫽ 0.109; P ⫽ .0001) and age ( ⫽ ⫺0.199; r2 ⫽ 0.147; P ⫽ .007) to be the main predictors for low albumin levels.
Correlation of glomerular filtration rate (GFR) and visceral fat ratios.
EVALUATION OF NUTRITIONAL STATUS
DISCUSSION
RT improves the prognosis for ESRD patients. Weight gain after RT is common; however, 15%–23% of recipients show symptoms of malnutrition.8,9 Significant weight gain in the post-transplant period is assumed to be a consequence of both increased appetite caused by steroid administration and correction of uremia that reconstitutes body components.6 Obesity is a well-known risk factor for morbidity and mortality in the general population; however, being overweight or obese is associated with increased survival among dialysis patients.4,10 The impact of weight gain on maintenance or loss of graft function graft function are controversial. In RT recipients. Body composition analysis with bioimpedance devices (BIA) is a useful noninvasive tool that can be used for early detection of malnutrition in both normal and chronic kidney disease populations;7 however, there are presently limited data on nutritional status and body composition measurements using BIA in RT recipients. Patients with varying degrees of renal function, show correlations of the values of free fat mass and body cell mass with 24-hour urinary creatinine excretion and muscle mass.11 Furthermore BIA has been documented to be suitable to measure total body fluids, while their is controversy about its use for body fat and fat-free mass estimates.11 The present study showed GFR values to positively correlate with fat and fat-free as well as muscle masses that suggesting graft function predicts nutritional status. Lucchesi et al11 observed reduced BMI values due to a reduction in fat mass, an alteration that was independent of the level of graft function. The present study indicated that development of malnutrition with a significant decrease in body weight and body fat followed by a decreased muscle mass were closely associated with declining graft function. We detected significant reductions in body weight, BMI, fat, and bone and muscle mass among patients with lower GFR values. Nevertheless, visceral fat ratio and BMI directly correlated with GFR. Similar to our results, Rettkowski et al6 observed impaired long-term kidney graft survival among patients with reduced nutritional status. Although they initially showed no significant influence of BMI on the risk of transplant failure at 1 year after kidney grafting, when patients were divided into 2 groups according to BMI, there was a clear disadvantage in long-term graft survival for subjects with BMI ⬍ 23.6 In contrast with our results, Ducloux et al12 demonstrated decreased GFR values among patients with substantial weight gain in the first year after RT. More than a 5% increase in BMI was associated with a 3-fold increase in the risk of graft loss. As a limitation of the study, they only used BMI rather than body composition analysis or anthropometric parameters.12 The conflicting data may be explained: patients with similar BMI may have different visceral fat, muscle, or bone mass. In another study, Marcen et al10 did not observe a significant difference between weight gain and survival among RT
1421
patients. In our study, RT patients with lower GFR values also demonstrated lower serum albumin levels, indicating that graft function was directly correlated with nutritional status. Similar to our results Mantoo et al. showed increased intact PTH levels, decreased albumin, mean serum bicarbonate, and hemoglobin levels that may eventually affect nutritional status to be related to renal allograft dysfunction. In addition they emphasized the impact of psychological factors, such as depression, socioeconomic factors, and inadequate dietary prescription to cause posttransplant malnutrition.13 Association of GFR loss and malnutrition is well known among predialysis chronic kidney disease patients. In a subanalysis of the Modification of Diet in Renal Disease study Kopple et al14 reported that patients with chronic renal disease shown progressively declining dietary protein and energy intakes as well as serum and anthropometric measures of protein– energy nutritional status GFR decreases. They also reported that low GFR levels were significantly associated with antrophometric markers of malnutrition like BMI and arm circumference. Similarly, Garg et al15 analyzed 5248 predialysis chronic kidney disease patients reporting that a GFR ⬍ 30 mL/min was independently associated with malnutrition after adjustment for relevant demographic, social, and medical conditions. We believe that dietary restrictions or increased medication needs in early phases of graft dysfunction can lead to a decreased appetite and food intake, these factors might decrease body weight including global decreases in all body compartments. Malnutrition accelerates due to progressive loss and addition of uremic symptoms of nausea, vomiting and decreased appetite. According to our findings especially in patients with moderate graft dysfunction (group 2), BIA was a useful noninvasive early diagnostic tool to diagnose malnutrition tendencies even before other markers like hypoalbuminemia become clinically apparent. In conclusion, we observed nutritional status to be closely related to loss of graft function among RT recipients. We suggest that BIA is an appropriate noninvasive diagnostic tool for early diagnosis of malnutrition in RT patients.
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